Deliberative Democracy in Practice: Handbooks on Commissioning, Facilitating, and Evaluating Deliberative Processes
Bibliographic record
Abstract
Governments seeking to address declining trust, increasing polarisation, and greater complexity in government policy have increasingly turned to democratic innovations to engage citizens. For practitioners and academics alike, the term ‘deliberative wave’ has become shorthand to describe the increased popularity of these tools and the emergence of a field of study that has the potential to revitalise citizen-state relationships. The practical handbooks reviewed here present a mosaic of tools, resources, and lessons from experience to ensure the successful commissioning, organisation, and facilitation of deliberative mini-publics (DMPs). They each provide valuable insights based on years of expertise developed running processes with publics (Enabling National Initiatives, Facilitating Deliberation) or consolidating a vast array of international experience (Assembling an Assembly, Innovative Citizen Participation, Evaluation Guidelines, Eight Ways to Institutionalise). In this review I reflect on definitions of deliberation; why these guides argue DMPs are important; and the connection between deliberative democracy theory and practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".